Estimating population lung cancer risk from radon using a resource efficient stratified population weighted sample survey protocol - Lessons and results from Ireland

A 2018 estimate indicates that there were 226,057 radon-attributable lung cancer deaths in 66 countries that had representative radon surveys. This is a shocking figure, and as it comes from only 66 countries it underestimates the worldwide death toll. Any research that enables countries to conduct representative radon surveys and to understand better the risk to citizens from radon is surely welcome. We hope this paper provides a useful methodology for estimating population risk. The estimation of population weighted average indoor radon levels requires statistically valid sampling methodologies that use a representative sample of occupied homes throughout the country. A literature review indicates that in many population weighted surveys, the sampling methodology may not have been designed to do this. This paper describes a simple, resource efficient methodology which produces statistically valid and reliable estimates based on a small scale sample that is representative of the population distribution. The resource efficient design of this study enables it to be repeated at frequent intervals providing for a longitudinal analysis of the population risk from indoor radon. This survey was conducted in Ireland using 653 measurements and a representative sampling strategy to provide a baseline population weighted radon exposure for future comparisons. This study estimates the average population weighted indoor radon concentration in Ireland to be 97.83 Bq m^-3 (95% Confidence Interval 90.69 Bq m^-3 to 105.53 Bq m^-3), and that there are an estimated 350 lung cancer cases and 255 deaths per year due to radon exposure. The mortality rate of 5.3 per 100,000 due to indoor radon, demonstrates that radon remains one of the highest preventable causes of death in Ireland.

The new Austrian indoor radon survey (ÖNRAP 2, 2013-2019): Design, implementation, results

The delineation of radon prone areas is one of the central requirements of the European Council Directive 2013/59/EURATOM. It is quite a complex task which usually requires the collection of radon data through an appropriate survey as a first step. This paper presents the design and methodology of the recent Austrian radon survey (ÖNRAP 2, 2013-2019) and its implementation. It details the results of the nationwide survey as well as correlations and dependencies with geology and building characteristics. The paper also discusses the representativeness of the survey as well as advantages and disadvantages of the selected approach. For the purpose of establishing a new delineation of radon prone areas in Austria we distributed approximately 75,000 passive long-term radon detectors. They were offered to selected members of the voluntary fire brigades and this resulted in about 50,000 radon measurements. Thus, a return rate of about 67% was achieved. The distribution of the radon results closely follows a log-normal distribution with a median of 99 Bq/m³, a geometric mean of 109 Bq/m³, and a geometric standard deviation factor of 2.29. 11% of the households show a mean radon concentration above the national reference level of 300 Bq/m³. Important data on building characteristics and the location of the measured rooms were collected by means of a specific questionnaire and a measurement protocol that were handed out together with the radon detectors. We were able to identify significant correlations between the indoor radon concentration and geology, the year of construction, and the coupling of the room to the ground (basement yes/no, floor level). Being a geographically-based and not a population-weighted survey, the comparison of building characteristics with the Austrian census data confirms that rural areas are over-represented in this survey. As a summary, the selected approach of conducting passive long-term radon measurements in selected dwellings of members of the voluntary fire brigades proved to be an efficient method to collect reliable data as a basis for the delineation of radon prone areas. The next step was to eliminate factors that influence the measured radon concentration through appropriate modelling. Based on the results predicted by the model radon areas are then be classified. This will be presented in a subsequent publication.

Developing a method for predicting radon concentrations above a reference level in new montenegrin buildings

Dependence of indoor radon concentrations (IRCs) in the ground floors of 1200 buildings across Montenegro on 11 factors was analyzed. A group of 734 buildings, for which none of the analyzed factors was missing, was further analyzed using the logistic regression method, in order to develop a prediction model for IRC occurrence above the national reference level for new buildings (200 Bq/m³). Applying the forward stepwise method, and based on likelihood ratios, five explanatory variables-municipality, type of building, presence of basement, window frames, and period of construction-were selected for including into the final logistic regression model for predicting probability of IRC > 200 Bq/m³. The final model explained 77.1% of the observed IRCs, while the obtained Area under the Curve of 0.8018 classified the model as having a very high predictive ability. Achieving similar values for both the final prediction model and the validation model, for sensitivity, specificity, and accuracy, confirmed the applicability of the developed model.